Disentangling Different Aspects of Change in Tests with the D-Diffusion Model

Jochen Ranger, Anett Wolgast, Sören Much, Augustin Mutak, Robert Krause, Steffi Pohl

Research output: Contribution to journalArticlepeer-review


Diffusion-based item response theory models are measurement models that link parameters of the diffusion model (drift rate, boundary separation) to latent traits of test takers. Similar to standard latent trait models, they assume the invariance of the test takers’ latent traits during a test. Previous research, however, suggests that traits change as test takers learn or decrease their effort. In this paper, we combine the diffusion-based item response theory model with a latent growth curve model. In the model, the latent traits of each test taker are allowed to change during the test until a stable level is reached. As different change processes are assumed for different traits, different aspects of change can be separated. We discuss different versions of the model that make different assumptions about the form (linear versus quadratic) and rate (fixed versus individual-specific) of change. In order to fit the model to data, we propose a Bayes estimator. Parameter recovery is investigated in a simulation study. The study suggests that parameter recovery is good under certain conditions. We illustrate the application of the model to data measuring visuo-spatial perspective-taking.

Original languageEnglish
Pages (from-to)1039-1055
Number of pages17
JournalMultivariate Behavioral Research
Issue number5
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 Taylor & Francis Group, LLC.


  • D-Diffusion model
  • change
  • growth curve model
  • item position effect
  • response time

ASJC Scopus subject areas

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)


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